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• Cube Edge enables AI collaboration between cloud and edge computing
• Use cases include satellite image analysis and oil field management
• Community effort is crucial to the project's growth
• Future plans involve creating dedicated scenario-based toolkits and supporting multiple architectures and operating systems

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• KubeEdge is a Kubernetes-based project for edge computing
• The stability test shows that KubeEdge can support 100 nodes and manage one million deployed pods
• The test results show impressive latency performance for both mutating and read-only API calls
• The presentation mentions plans to improve security and device mappers, as well as support for cross-submarine communications and edge clusters
• The KubeEdge project is collaborating with telecom companies and contributing to white papers on 5G multi-access edge computing

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• KubeEdge has brought cloud-native to space with several small research satellites equipped with edge computing and AI.
• KubeEdge Sedna and TinyMS are combined to enable incremental learning at the satellite for tasks like remote sensing and earth observing.
• The presentation provides a deep dive into how KubeEdge Sedna and TinyMS work together to accomplish incremental deep learning for satellites.
• The use of KubeEdge Sedna and TinyMS allows for the optimization of vast amounts of data using artificial intelligence and cloud-native technology.
• The presentation includes an anecdote about how KubeEdge Sedna and TinyMS are used to detect farmland areas using satellite data.

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• Kubernetes can be deployed on edge nodes transparently to developers
• Active-active deployment can prevent connection loss between cloud and edge
• IoT cases involve deploying apps on edge nodes
• Performance testing includes latency, throughput, scalability, CPU usage, and memory usage
• Kubernetes scalability is multi-dimensional and requires careful configuration
• Decentralized security and network policy are being researched for edge nodes

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• The Sedna project is an AI toolkit for age cloud collaboration and synergy mechanism for AI workloads
• The project simplifies incremental learning and enhances the federation of federated learning to support more scenarios for the robotic SIG
• The Sedna project helps achieve joint influencing for day-to-day AI workload and simplifies the model training upgrade iteration
• The project focuses on API definition and the reference architecture as well as implementation relevant to the robotics ecosystem
• The Sedna project may focus on containerizing some of the software including the iOS and the engageable
• The project is used in the world's longest process C bridge to monitor metrics of the bridge itself and track traffic to generate emergency alerts
• The Sedna project aims to achieve age cloud collaborative architecture or robot cloud collaborative architecture

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• Introduction to cloud native GitHub stores such as ArgoCD and Flux
• Real use cases of GitOps in infrastructure provisioning using ClusterAPI and FlexCD integration
• Pushing applications towards the edge using KubeEdge and ArgoCD
• Importance of declarative, automatic, and reliable fundamentals in cloud native deployment